IP Library › Granted Patent US 11,535,255
Granted Patent B2
US 11,535,255 · App. 16/288,781 · Granted Dec 27, 2022

Micro-weather reporting

Inventors: Aghyad Saleh (Grand Prairie, TX); Jason Schell (Dallas, TX); Neil Dutta (Addison, TX)
Assignee: Toyota Motor North America, Inc.
B60W40/02G01C21/3691G01W1/10G05D1/0088G05D1/0295
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Quick Facts
Patent No.
US 11,535,255
App. No.
16/288,781
Granted
Dec 27, 2022
Kind
B2
Abstract

Systems and methods for vehicle-based weather detection are disclosed herein. The systems and methods can include selecting one or more vehicles from a plurality of vehicles based on one or more network membership parameters. One or more data acquisition networks can be formed using the one or more selected vehicles. Sensor data can be received from the one or more data acquisition networks. One or more weather conditions can be predicted using the sensor data. One or more environmental elements can be updated based on the predicted weather conditions.

Claims (37)

1. A collaborative forecasting system for vehicle-based weather detection, comprising:

one or more processors; and

a memory communicably coupled to the one or more processors and storing:

a coordination module including instructions that when executed by the one or more processors cause the one or more processors to:

select one or more vehicles based on one or more network membership parameters; and

form one or more data acquisition networks using the one or more selected vehicles;

a prediction module including instructions that when executed by the one or more processors cause the one or more processors to:

receive sensor data from the one or more data acquisition networks based on one or more detection parameters; and

predict one or more weather conditions using the sensor data; and

a response module including instructions that when executed by the one or more processors cause the one or more processors to update one or more environmental elements based on the one or more predicted weather conditions, the environmental elements include at least one of a weather map, a vehicle route, advice based on one or more vehicle limitations, travel time estimates, and control system recommendations.

2. The collaborative forecasting system of claim 1 , wherein the response module further comprises instructions to control an autonomous driving module of one or more of the one or more selected vehicles based on the one or more updated environmental elements.

3. The collaborative forecasting system of claim 1 , wherein the one or more network membership parameters include at least one of vehicle generation, sensor reliability, and sensor availability.

4. The collaborative forecasting system of claim 1 , wherein the prediction module further comprises instructions to transmit the sensor data to a lead vehicle in the one or more data acquisition networks.

5. The collaborative forecasting system of claim 1 , wherein the sensor data comprises sensor data from one or more implicit sensors.

6. The collaborative forecasting system of claim 1 , wherein the one or more data acquisition networks are a mesh network, wherein the one or more data acquisition networks coordinate at least one of collection of the sensor data, processing of the sensor data, and transmission of the sensor data.

7. The collaborative forecasting system of claim 1 , wherein the prediction module further comprises instructions to compare predicted weather conditions between a plurality of data acquisition networks.

8. A non-transitory computer-readable medium for vehicle-based weather detection and storing instructions that when executed by one or more processors cause the one or more processors to:

select one or more vehicles from a plurality of vehicles based on one or more network membership parameters;

form one or more data acquisition networks using the one or more selected vehicles;

receive sensor data from the one or more data acquisition networks based on one or more detection parameters;

predict one or more weather conditions using the sensor data; and

update one or more environmental elements based on the one or more predicted weather conditions, the environmental elements include at least one of a weather map, a vehicle route, advice based on one or more vehicle limitations, travel time estimates, and control system recommendations.

9. The non-transitory computer-readable medium of claim 8 , further comprising instructions to control an autonomous driving module of one or more of the one or more selected vehicles based on the one or more updated environmental elements.

10. The non-transitory computer-readable medium of claim 8 , wherein the one or more network membership parameters include at least one of vehicle generation, sensor reliability, and sensor availability.

11. The non-transitory computer-readable medium of claim 8 , further comprising instructions to transmit the sensor data to a lead vehicle in the one or more data acquisition networks.

12. The non-transitory computer-readable medium of claim 8 , wherein the sensor data comprises sensor data from one or more implicit sensors.

13. The non-transitory computer-readable medium of claim 8 , further comprising instructions to compare predicted weather conditions between a plurality of data acquisition networks.

14. A method for vehicle-based weather detection, comprising:

selecting one or more vehicles from a plurality of vehicles based on one or more network membership parameters;

forming one or more data acquisition networks using the one or more selected vehicles;

receiving sensor data from the one or more data acquisition networks based on one or more detection parameters;

predicting one or more weather conditions using the sensor data; and

updating one or more environmental elements based on the predicted weather conditions the environmental elements include at least one of a weather map, a vehicle route, advice based on one or more vehicle limitations, travel time estimates, and control system recommendations.

15. The method of claim 14 , further comprising controlling an autonomous driving module of one or more of the one or more selected vehicles based on the updated one or more environmental elements.

16. The method of claim 14 , wherein the one or more network membership parameters include at least one of vehicle generation, sensor reliability, and sensor availability.

17. The method of claim 14 , further comprising transmitting the sensor data to a lead vehicle in the one or more data acquisition networks.

18. The method of claim 14 , further comprising comparing predicted weather conditions between a plurality of data acquisition networks.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 4, 2019
From: SALEH, AGHYAD; SCHELL, JASON; DUTTA, NEIL
To: TOYOTA MOTOR NORTH AMERICA, INC.
Reel/Frame 048493/0348 →
Continuity (1)
Related Publication 20200276977A1 · Sep 3, 2020